Pinus taeda plantations in South America have been cultivated on soils of low natural fertility, especially phosphorus (P), increasing the necessity for phosphate fertilisation. The proposition of critical levels (CL) and sufficiency ranges (SR) of P in the needles and soil in P. taeda plantations can assist in decision making on the rational use of phosphate fertilisers, reducing the cost of production and P fertiliser losses. This study aimed to propose critical levels and sufficiency ranges of P in P. taeda plantations in southern Brazil. The study was conducted in 12 plantations of P. taeda subject to fertilisation and non-fertilisation. The parameters height, diameter at breast height, mean annual increment (MAI), P concentration in the needles, available P in the soil, pH, soil organic matter, sand and clay were evaluated at mid-rotation and end-of-rotation. The CL and SR of P in the needles and soil were calculated. The use of a Bayesian approach to boundary analysis allowed us to propose values for CL of P in the needles and soil in P. taeda plantations in southern Brazil. The CL of P in the needles relative to the MAI was 1.6 g P kg-1. The CL for P in the soil was 5.2 mg P dm-3. The use of CL will allow foresters to reverse any P deficiency, ensuring adherence to the adjusted phosphate fertiliser recommendations, reducing production costs and possible P fertiliser losses in P. taeda plantations.
This study aimed to evaluate the effect of boron treatment and thermal modification on the wood colour of African mahogany species (Khaya ivorensis, K. senegalensis and K. grandifoliola) and eucalyptus (Eucalyptus grandis & times; E. urophylla) intended for sawnwood production. For this purpose, four logs measuring 50 cm in length were sampled along the tree trunk of the selected species, and 3-cm-thick discs were collected at the base of each log, to evaluate the basic density and its variation along the stem. The logs were converted into samples of 2.3 cm & times; 10.0 cm & times; 47.0 cm (thickness & times; width & times; length), which underwent treatments consisting of the control (no treatment), chemical treatment (boric acid and borax) using a 4% solution in which the samples were immersed until reaching a minimum retention of 0.5%, expressed as boric acid equivalent, and thermal modification (180 and 200 degrees C), applied for 120 min at the maximum temperature. The basic wood density varied along the trunk of the trees, particularly in K. grandifoliola (0.450-0.410 g cm-3), with K. senegalensis (0.542 g cm-3) showing density values similar to those of E. grandis & times; E. urophylla (0.586 g cm-3). Colourimetric analysis revealed that the treatments (chemical and thermal) promoted few variations in the colours (Delta E*) of the tangential (T) and radial (R) faces of the wood, except for the treatment at 200 degrees C, which caused darkening of the wood (K. ivorensis (Delta E*T = 10.01 - Delta E*R = 18.03), K. senegalensis (Delta E*T = 15.70 - Delta E*R = 21.25), K. grandifoliola (Delta E*T = 7.99 - Delta E*R = 11.76), and eucalyptus (Delta E*T = 9.50 - Delta E*R = 9.82)). However, the heat treatments standardised the color of the heartwood and sapwood.
Mapping the structure of commercial forests using hyperspectral remote sensing techniques produces highly accurate and very fine ecological details that are vital for sustainable forest management and planning. However, the implementation of traditional hyperspectral modelling approaches has been hindered by issues of multicollinearity and data dimensionality. Thus, this study aimed to examine the utility of a hybrid partial least squares regression and random forest (PLSR-RF) algorithm in accurately predicting four structural attributes (basal area, diameter at breast height, tree height and site index) amongst varieties of commercial eucalyptus and acacia using airborne AISA Eagle hyperspectral imagery (2.4 m spatial resolution). Additionally, two traditional hyperspectral modelling methodologies, PLSR and RF, were utilised alongside the hybrid PLSR-RF method. The best model obtained for this study was produced for Acacia mearnsii using the hybrid PLSR-RF algorithm (R 2 = 0.92 and RMSE = 0.02). The results of this study indicate that the hybrid approach is a robust and promising model for the estimation and mapping of forest structure for varieties of eucalyptus and acacia within a commercial plantation. In addition, the hybrid model predominantly outperformed the traditional modelling approaches. The hybrid approach successfully navigates the issues of multicollinearity and data dimensionality associated with hyperspectral remote sensing through the combination of PLSR and RF. The generation of in-depth structural information provided by hyperspectral data supports more effective and timely forest management and planning practices.
